Improving multimodel weather forecast of monsoon rain over China using FSU superensemble

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are: ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.     

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.     

基于 ＩＰＣＣ-ＡＲ４模式资料的地面气温超级集合预测

利用参与IPCC-AR4的8个全球气候系统模式对20世纪气候模拟情景下地面气温的模拟结果,对其进行多模式集成处理。在此基础上,对这些全球气候系统模式在各种能源之间的平衡（A1B）情景下2010—2030年的地面气温进行多模式超级集合预测。结果发现,8个全球气候系统模式模拟的地面温度均方根误差都比多模式简单集合平均的大。超级集合相对于各个模式及简单集合平均的模拟效果更好,其均方根误差比最好的模式误差减小了13℃。在A1B情景下,超级集合预测未来20 a北半球平均地面气温将普遍升高,大洋上的增温幅度比陆地上小。中国东部地区以及青藏高原、新疆大部未来20 a气温将明显升高,内蒙东部和辽宁西部最高升温可达20~24 ℃,其余地区升温在20 ℃以内。     

北美洲冬季冷空气对南美洲夏季降水异常影响的研究

利用1981—2017年NCEP/NCAR再分析资料和ECMWF再分析资料,研究了北美洲冬季高纬度冷空气对南美洲夏季降水异常的影响。结果表明,北美洲冬季高纬度冷空气通过影响向南越赤道气流的强弱,影响南美洲热带辐合带（intertropical convergence zone, ITCZ）位置和强度的变化,进一步引起南美洲天气的变化。北美洲冬季冷空气的南下过程能够引起80°～70°W的向南越赤道气流明显加强,导致2011年南美洲热带辐合带的位置异常偏南,强度异常偏强,是造成降水异常偏多的重要成因。通过相关分析发现北美洲冬季冷空气对南美洲ITCZ位置的影响更明显。     

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

本研究基于新一代FGOALS-f2动力集合预测系统35年(1981-2015年)的热带气旋历史回报试验对南海台风季(7-11月)热带气旋活动超前10天的月预测技巧进行评估,并对2020年南海台风季热带气旋活动进行了实时月预测尝试.结果表明:FGOALS-f2能较好地预测南海热带气旋路径密度演变特征,预测的热带气旋生成个...     

Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.     

Simulation capability of tropical and extratropical seasonal climate anomalies over South America

An ensemble of 20 extended integrations of the atmospheric model CSIRO Mark 2, forced with the sea-surface temperature observed during the 1986–1998 period, was performed to analyze the simulation capability of seasonal climate anomalies over South America and adjacent oceanic areas. Variations of the simulation skill within the region and during the experimental period were assessed through standard statistical measures and compared to the signal-to-noise ratio distribution. Before the skill assessment, model systematic errors were thoroughly evaluated. The results confirm that the simulation skill is very high in tropical oceanic areas, and decreases rapidly towards middle and high latitudes. Model performance at mid and high atmospheric levels is substantially better than at low levels. Relatively high simulation capability was found over the Pacific Ocean between the equator and the Antarctic coast, which is coherent with the presence of three relative maximums in the signal-to-noise ratio, similar to the increase of the forced variance found by several authors over much of the Pacific–North American pattern region. Rainfall rate and second-order moments associated with the cyclonic activity and the meridional eddy fluxes of heat and humidity are better simulated in a narrow strip parallel to the SPCZ and extending further southeast into mid latitudes of the continent. The simulation skill noticeably improves during the warm and cold ENSO phases, in correspondence with an intensification of the signal-to-noise ratio, and useful rainfall anomaly simulations can be obtained over the Amazonas and Rio de la Plata river basins.     

Dynamic downscaling of summer precipitation prediction over China in 1998 using WRF and CCSM4

To study the prediction of the anomalous precipitation and general circulation for the summer(June–July–August) of1998, the Community Climate System Model Version 4.0(CCSM4.0) integrations were used to drive version 3.2 of the Weather Research and Forecasting(WRF3.2) regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.     

GCM sensitivity experiments with locally modified land surface properties over tropical South America

Ensembles of 1-year-long experiments with a relatively high-resolution ECMWF model were conducted in order to investigate the impact of modified land surface properties on local, regional and large-scale atmospheric circulations. The modifications consisted of changes to land cover and increased albedo over the northern part of South America. In many respects the experimental design resembles the setting of classical deforestation experiments. The local model response to imposed modifications, which includes a reduction in precipitation as well as in evaporation and an increase in surface temperature, was found to be stronger in dry (July–September, JAS) than in wet (January–March, JFM) season, and in the ensemble with higher albedo value. Local drying is discussed in terms of locally generated overturning that resembles a direct thermal circulation. The effects of this circulation seem to be dominant over the reduction in large-scale moisture supply from the adjacent ocean. On large scales, changes to the Pacific branch of the Walker circulation lead, through modified divergent flow, to a tropics-wide impact on precipitation. In addition to South America, the largest changes are seen in the south Pacific convergence zone in JFM, while the impact on the Atlantic inter-tropical convergence zone is stronger in JAS. In the extratropics, there is little change in precipitation. In the upper troposphere, a distinctive teleconnection wave-pattern could be seen in the Pacific/North American region during JFM. A notable feature in the upper-air model response in JAS is a wave train extending from South America, over the northern Atlantic into Europe. With regard to the interaction between the land surface response and model systematic errors, our results suggest that the erroneous shift of the downward branch of the Pacific/South American Walker circulation is likely to be a cause, rather than a consequence, of the rainfall deficit over South America in the model climatology.     

Future projections of winter rainfall in southeast Australia using a statistical downscaling technique

Much of southeast Australia has experienced rainfall substantially below the long-term average since 1997. This protracted drought is particularly noticeable in those parts of South Australia and Victoria which experience a winter (May through October) rainfall peak. For the most part, the recent meteorological drought has affected the first half of the rainfall season May–June–July (MJJ), while rainfall during the second half August–September–October (ASO) has been much closer to the long term average. The recent multi-year drought is without precedent in the instrumental record, and is qualitatively similar to the abrupt decline in rainfall which was observed in the southwest of Western Australia in the 1960 and 1970s. Using a statistical downscaling technique, the rainfall decline is linked to observed changes in large-scale atmospheric fields (mean sea level pressure and precipitable water). This technique is able to reproduce the statistical properties of rainfall in southeast Australia, including the interannual variability and longer time-scale changes. This has revealed that the rainfall recent decline may be explained by a shift to higher pressures and lower atmospheric precipitable water in the region. To explore the likely future evolution of rainfall in southeast Australia under human induced climate change, the same statistical downscaling technique is applied to five climate models forced with increasing greenhouse gas concentrations. This reveals that average rainfall in the region is likely to decline in the future as greenhouse gas concentrations increase, with the greatest decline occurring during the first half of winter. Projected declines vary amongst models but are generally smaller than the recent early winter rainfall deficits. In contrast, the rainfall decline in late winter–spring is larger in future projections than the recent rainfall deficits have been. We illustrate the consequences of the observed and projected rainfall declines on water supply to the major city of Melbourne, using a simple rainfall run-off relationship. This suggests that the water resources may be dramatically affected by future climate change, with percentage reductions approximately twice as large as corresponding changes in rainfall.     

Low Frequency Variability of Rainfall in Southeastern South America

Summary The climatic variations of precipitation, within the period range of 10 and 100 years, in the Southeastern South America (SSA) region are investigated. A gaussian low-pass filter with a cut-off period at 30 years, was applied to annual precipitation records, in order to smooth high frequencies and to make evident the variability within the searched low frequency ranges (longer-than-30 years period). The principal components analysis (PCA) applied to filtered series allowed the determination of two climatic signals, defined by the first and second principal components (PC ₁ and PC ₂). Both signals reflect remarkable changes in the annual precipitation values (a sustained increase in precipitation), over a large region of SSA (great part of Argentine pampas and Uruguay). The PCA applied to the filtered precipitation records for each quarter, which we have associated to seasonal rainfall, allowed the determination of the incidence of PC ₁ and PC ₂ on the variability of the series. In particular, it is in the summer rainfall where the two first seasonal components, (PC1₁ and PC1₂), show a high degree of similarity (correlations >0.8) with the respective annual components PC ₁ and PC ₂. During the spring season only the first component, (PC4₁), shows high correlation with PC ₁; the rest of the seasonal components do not show correlations exceeding the established values of 0.8. Received May 5, 1997 Revised April 26, 1998     

多尺度残差金字塔网络模型三维气象要素降尺度研究

本文将研究对象从地面气象要素扩展到三维气象要素;针对三维气象要素空间分辨率提升问题,融合多气象要素交互影响、多尺度作用、多气压层气象要素天气系统配置作用等机理,提出一种多尺度残差拉普拉斯金字塔网络三维气象要素深度学习降尺度模型(Multi-Scale Residual Laplacian Pyramid Network, MSRLapN)。具体地,构造一种多尺度残差模块(Multi-Scale Resolution Block, MSRB),用于从三维空间多种气象要素中自动提取预报特征;从机器学习领域引入多尺度金字塔技术描述气象要素的多尺度交互作用;然后,通过超分辨重建循环迭代方法,基于大样本历史数据学习订正降尺度预报的误差。在华东气候区,本文针对相对湿度和风速两种气象要素,实现了7种前沿深度学习超分辨降尺度方法的三维空间气象要素降尺度,并将本文提出的方法与这7种深度学习超分辨降尺度方法与之对比,实验结果表明：MSRB模块可从数据中有效提取三维气象要素多尺度作用的特征信息,MSRLapN有效实现了三维气象要素降尺度,效果优于其他对比方法。     

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

Climate change projections over India by a downscaling approach using PRECIS

This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre’s 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.